Coping with Combinatorial Uncertainty in Word Learning: A Flexible Usage-Based Mode
Pieter Wellens · VUBIR (Vrije Universiteit Brussel) · 2008
Agents in the process of bootstrapping a shared lexicon face immense uncertainty. The problem that an agent cannot point to meaning but only to objects, represents one of the core aspects of the problem. Even with a straightforward representation of meaning, such as a set of boolean features, the hypothesis space scales exponential in the number of primitive features. Furthermore, data suggests that human learners grasp aspects of many novel words after only a few exposures. We propose a model that can handle the exponential increase in uncertainty and allows scaling towards very large meaning spaces. The key novelty is that word learning or bootstrapping should not be viewed as a mapping task, in which a set of forms is to be mapped onto a set of (predefined) concepts. Instead we view word learning as a process in which the representation of meaning gradually shapes itself, while being usable in interpretation and production almost instantly.